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- from itertools import chain
- from typing import Optional, Dict, Iterable, Set, NamedTuple
- import pickle
- import os
- from enum import Enum
- from config import Config
- import tensorflow as tf
- from argparse import Namespace
- from common import common
- class VocabType(Enum):
- Token = 1
- Target = 2
- Path = 3
- SpecialVocabWordsType = Namespace
- _SpecialVocabWords_OnlyOov = Namespace(
- OOV='<OOV>'
- )
- _SpecialVocabWords_SeparateOovPad = Namespace(
- PAD='<PAD>',
- OOV='<OOV>'
- )
- _SpecialVocabWords_JoinedOovPad = Namespace(
- PAD_OR_OOV='<PAD_OR_OOV>',
- PAD='<PAD_OR_OOV>',
- OOV='<PAD_OR_OOV>'
- )
- class Vocab:
- def __init__(self, vocab_type: VocabType, words: Iterable[str],
- special_words: Optional[SpecialVocabWordsType] = None):
- if special_words is None:
- special_words = Namespace()
- self.vocab_type = vocab_type
- self.word_to_index: Dict[str, int] = {}
- self.index_to_word: Dict[int, str] = {}
- self._word_to_index_lookup_table = None
- self._index_to_word_lookup_table = None
- self.special_words: SpecialVocabWordsType = special_words
- for index, word in enumerate(chain(common.get_unique_list(special_words.__dict__.values()), words)):
- self.word_to_index[word] = index
- self.index_to_word[index] = word
- self.size = len(self.word_to_index)
- def save_to_file(self, file):
- # Notice: From historical reasons, a saved vocab doesn't include special words.
- special_words_as_unique_list = common.get_unique_list(self.special_words.__dict__.values())
- nr_special_words = len(special_words_as_unique_list)
- word_to_index_wo_specials = {word: idx for word, idx in self.word_to_index.items() if idx >= nr_special_words}
- index_to_word_wo_specials = {idx: word for idx, word in self.index_to_word.items() if idx >= nr_special_words}
- size_wo_specials = self.size - nr_special_words
- pickle.dump(word_to_index_wo_specials, file)
- pickle.dump(index_to_word_wo_specials, file)
- pickle.dump(size_wo_specials, file)
- @classmethod
- def load_from_file(cls, vocab_type: VocabType, file, special_words: SpecialVocabWordsType) -> 'Vocab':
- special_words_as_unique_list = common.get_unique_list(special_words.__dict__.values())
- # Notice: From historical reasons, a saved vocab doesn't include special words,
- # so they should be added upon loading.
- word_to_index_wo_specials = pickle.load(file)
- index_to_word_wo_specials = pickle.load(file)
- size_wo_specials = pickle.load(file)
- assert len(index_to_word_wo_specials) == len(word_to_index_wo_specials) == size_wo_specials
- min_word_idx_wo_specials = min(index_to_word_wo_specials.keys())
- if min_word_idx_wo_specials != len(special_words_as_unique_list):
- raise ValueError(
- "Error while attempting to load vocabulary `{vocab_type}` from file `{file_path}`. "
- "The stored vocabulary has minimum word index {min_word_idx}, "
- "while expecting minimum word index to be {nr_special_words} "
- "because having to use {nr_special_words} special words, which are: {special_words}. "
- "Please check the parameter `config.SEPARATE_OOV_AND_PAD`.".format(
- vocab_type=vocab_type, file_path=file.name, min_word_idx=min_word_idx_wo_specials,
- nr_special_words=len(special_words_as_unique_list), special_words=special_words))
- vocab = cls(vocab_type, [], special_words)
- vocab.word_to_index = {**word_to_index_wo_specials,
- **{word: idx for idx, word in enumerate(special_words_as_unique_list)}}
- vocab.index_to_word = {**index_to_word_wo_specials,
- **{idx: word for idx, word in enumerate(special_words_as_unique_list)}}
- vocab.size = size_wo_specials + len(special_words_as_unique_list)
- return vocab
- @classmethod
- def create_from_freq_dict(cls, vocab_type: VocabType, word_to_count: Dict[str, int], max_size: int,
- special_words: Optional[SpecialVocabWordsType] = None):
- if special_words is None:
- special_words = Namespace()
- words_sorted_by_counts = sorted(word_to_count, key=word_to_count.get, reverse=True)
- words_sorted_by_counts_and_limited = words_sorted_by_counts[:max_size]
- return cls(vocab_type, words_sorted_by_counts_and_limited, special_words)
- @staticmethod
- def _create_word_to_index_lookup_table(word_to_index: Dict[str, int], default_value: int):
- return tf.lookup.StaticHashTable(
- tf.lookup.KeyValueTensorInitializer(
- list(word_to_index.keys()), list(word_to_index.values()), key_dtype=tf.string, value_dtype=tf.int32),
- default_value=tf.constant(default_value, dtype=tf.int32))
- @staticmethod
- def _create_index_to_word_lookup_table(index_to_word: Dict[int, str], default_value: str) \
- -> tf.lookup.StaticHashTable:
- return tf.lookup.StaticHashTable(
- tf.lookup.KeyValueTensorInitializer(
- list(index_to_word.keys()), list(index_to_word.values()), key_dtype=tf.int32, value_dtype=tf.string),
- default_value=tf.constant(default_value, dtype=tf.string))
- def get_word_to_index_lookup_table(self) -> tf.lookup.StaticHashTable:
- if self._word_to_index_lookup_table is None:
- self._word_to_index_lookup_table = self._create_word_to_index_lookup_table(
- self.word_to_index, default_value=self.word_to_index[self.special_words.OOV])
- return self._word_to_index_lookup_table
- def get_index_to_word_lookup_table(self) -> tf.lookup.StaticHashTable:
- if self._index_to_word_lookup_table is None:
- self._index_to_word_lookup_table = self._create_index_to_word_lookup_table(
- self.index_to_word, default_value=self.special_words.OOV)
- return self._index_to_word_lookup_table
- def lookup_index(self, word: tf.Tensor) -> tf.Tensor:
- return self.get_word_to_index_lookup_table().lookup(word)
- def lookup_word(self, index: tf.Tensor) -> tf.Tensor:
- return self.get_index_to_word_lookup_table().lookup(index)
- WordFreqDictType = Dict[str, int]
- class Code2VecWordFreqDicts(NamedTuple):
- token_to_count: WordFreqDictType
- path_to_count: WordFreqDictType
- target_to_count: WordFreqDictType
- class Code2VecVocabs:
- def __init__(self, config: Config):
- self.config = config
- self.token_vocab: Optional[Vocab] = None
- self.path_vocab: Optional[Vocab] = None
- self.target_vocab: Optional[Vocab] = None
- # Used to avoid re-saving a non-modified vocabulary to a path it is already saved in.
- self._already_saved_in_paths: Set[str] = set()
- self._load_or_create()
- def _load_or_create(self):
- assert self.config.is_training or self.config.is_loading
- if self.config.is_loading:
- vocabularies_load_path = self.config.get_vocabularies_path_from_model_path(self.config.MODEL_LOAD_PATH)
- if not os.path.isfile(vocabularies_load_path):
- raise ValueError(
- "Model dictionaries file is not found in model load dir. "
- "Expecting file `{vocabularies_load_path}`.".format(vocabularies_load_path=vocabularies_load_path))
- self._load_from_path(vocabularies_load_path)
- else:
- self._create_from_word_freq_dict()
- def _load_from_path(self, vocabularies_load_path: str):
- assert os.path.exists(vocabularies_load_path)
- self.config.log('Loading model vocabularies from: `%s` ... ' % vocabularies_load_path)
- with open(vocabularies_load_path, 'rb') as file:
- self.token_vocab = Vocab.load_from_file(
- VocabType.Token, file, self._get_special_words_by_vocab_type(VocabType.Token))
- self.target_vocab = Vocab.load_from_file(
- VocabType.Target, file, self._get_special_words_by_vocab_type(VocabType.Target))
- self.path_vocab = Vocab.load_from_file(
- VocabType.Path, file, self._get_special_words_by_vocab_type(VocabType.Path))
- self.config.log('Done loading model vocabularies.')
- self._already_saved_in_paths.add(vocabularies_load_path)
- def _create_from_word_freq_dict(self):
- word_freq_dict = self._load_word_freq_dict()
- self.config.log('Word frequencies dictionaries loaded. Now creating vocabularies.')
- self.token_vocab = Vocab.create_from_freq_dict(
- VocabType.Token, word_freq_dict.token_to_count, self.config.MAX_TOKEN_VOCAB_SIZE,
- special_words=self._get_special_words_by_vocab_type(VocabType.Token))
- self.config.log('Created token vocab. size: %d' % self.token_vocab.size)
- self.path_vocab = Vocab.create_from_freq_dict(
- VocabType.Path, word_freq_dict.path_to_count, self.config.MAX_PATH_VOCAB_SIZE,
- special_words=self._get_special_words_by_vocab_type(VocabType.Path))
- self.config.log('Created path vocab. size: %d' % self.path_vocab.size)
- self.target_vocab = Vocab.create_from_freq_dict(
- VocabType.Target, word_freq_dict.target_to_count, self.config.MAX_TARGET_VOCAB_SIZE,
- special_words=self._get_special_words_by_vocab_type(VocabType.Target))
- self.config.log('Created target vocab. size: %d' % self.target_vocab.size)
- def _get_special_words_by_vocab_type(self, vocab_type: VocabType) -> SpecialVocabWordsType:
- if not self.config.SEPARATE_OOV_AND_PAD:
- return _SpecialVocabWords_JoinedOovPad
- if vocab_type == VocabType.Target:
- return _SpecialVocabWords_OnlyOov
- return _SpecialVocabWords_SeparateOovPad
- def save(self, vocabularies_save_path: str):
- if vocabularies_save_path in self._already_saved_in_paths:
- return
- with open(vocabularies_save_path, 'wb') as file:
- self.token_vocab.save_to_file(file)
- self.target_vocab.save_to_file(file)
- self.path_vocab.save_to_file(file)
- self._already_saved_in_paths.add(vocabularies_save_path)
- def _load_word_freq_dict(self) -> Code2VecWordFreqDicts:
- assert self.config.is_training
- self.config.log('Loading word frequencies dictionaries from: %s ... ' % self.config.word_freq_dict_path)
- with open(self.config.word_freq_dict_path, 'rb') as file:
- token_to_count = pickle.load(file)
- path_to_count = pickle.load(file)
- target_to_count = pickle.load(file)
- self.config.log('Done loading word frequencies dictionaries.')
- # assert all(isinstance(item, WordFreqDictType) for item in {token_to_count, path_to_count, target_to_count})
- return Code2VecWordFreqDicts(
- token_to_count=token_to_count, path_to_count=path_to_count, target_to_count=target_to_count)
- def get(self, vocab_type: VocabType) -> Vocab:
- if not isinstance(vocab_type, VocabType):
- raise ValueError('`vocab_type` should be `VocabType.Token`, `VocabType.Target` or `VocabType.Path`.')
- if vocab_type == VocabType.Token:
- return self.token_vocab
- if vocab_type == VocabType.Target:
- return self.target_vocab
- if vocab_type == VocabType.Path:
- return self.path_vocab
|